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This article investigates the problem of scale factor error estimation for missiles with a strapdown seeker. We first theoretically prove that the seeker scale factor error will introduce a parasitic loop in the guidance loop and, hence, result in instability of the guidance loop. This fact reveals that it is necessary to extract the required guidance information and estimate the scale factor error simultaneously in real time. To this end, the observability of scale factor error estimation is analyzed, and the theoretical results show that this information is weakly or poorly observable under conventional proportional navigation guidance. To address this problem, a new optimal guidance law that can be utilized to improve the estimation performance is proposed by optimizing terminal miss distance, control energy, and observability in an integrated manner. The characteristics of the proposed optimal guidance law are also theoretically analyzed to provide better insights of the proposed approach. Extensive numerical simulations are conducted to support the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this article, a minimum-fuel powered-descent optimal guidance algorithm that incorporates obstacle avoidance is presented. The approach is based on convex optimization that includes the obstacles using nonconvex functions. To convert these nonconvex obstacle constraints to convex ones, a simple linearization procedure is employed. It is proved that the optimal solution of the convex relaxation problem is also optimal for the original nonconvex one. The sensitivity of the multiobstacle avoidance method to the relaxation factor and its effectiveness under different conditions are also investigated through simulations. [ABSTRACT FROM AUTHOR]

One of the main challenges posed by the next space systems generation is the high level of autonomy they will require. Hazard Detection and Avoidance is a key technology in this context. An adaptive guidance algorithm for landing that updates the trajectory to the surface by means of an optimal control problem solving is here presented. A semi-analytical approach is proposed. The trajectory is expressed in a polynomial form of minimum order to satisfy a set of boundary constraints derived from initial and final states and attitude requirements. By imposing boundary conditions, a fully determined guidance profile is obtained, function of a restricted set of parameters. The guidance computation is reduced to the determination of these parameters in order to satisfy path constraints and other additional constraints not implicitly satisfied by the polynomial formulation. The algorithm is applied to two different scenarios, a lunar landing and an asteroidal landing, to highlight its general validity. An extensive Monte Carlo test campaign is conducted to verify the versatility of the algorithm in realistic cases, by the introduction of attitude control systems, thrust modulation, and navigation errors. The proposed approach proved to be flexible and accurate, granting a precision of a few meters at touchdown. [ABSTRACT FROM AUTHOR]

Abstract: The paper discusses a predictive guidance technique for the optimal aerocapture maneuver accomplished by a high-mass Mars orbiter to achieve the targeted circular parking orbit with minimal fuel consumption. Unlike the majority of investigated algorithms of the “predictor–corrector” family with a time-invariant control, the guidance algorithm considered in the paper computes a time-dependent control function which, being a reference profile of the angle of attack, provides a minimum to the post-aerocapture propulsive ΔV at the apoapsis of a transfer ellipse. Initially, the two-parametric bounded linear control function has been used, and the algorithm developed found an optimal solution directly by “descending” in a control parameters space. The simulation has shown that an optimum is achieved when the slope of the linear function tends to infinity, i.e., the bounded linear control is being transformed into a one-parametric step function. Such a result proved to be in conformity with the earlier “indirect” optimal solution, namely, derived from Pontryaginʼs maximum principle for a slightly simplified motion model. Thus, the final version of algorithm providing “direct” optimization by solving a nonlinear eigenvalue problem needs to determine only one parameter – switching time of angle of attack. The solution obtained satisfies the terminal condition – a target apoapsis, as well as it is close to optimum. Another issue the paper focuses on is an improvement of robustness of the algorithm under unpredictable model disturbances, especially variations of atmospheric density. The new model adaptation algorithm is developed, which estimates density errors and updates the density profile during the descent phase to be used for control corrections in the ascent phase. Testing the example simulation model with angular motion has shown the runtime effectiveness and high targeting accuracy provided by the proposed predictive technique. [Copyright &y& Elsevier]